dxa.rar_DXA_分型动画算法

function [Fxy] = DCCA(x, y, Scales, Q) % function [Fxy] =DCCA(x, y, Scales, Q) % for ns = 1:length(Scales) I = 1:length(x)-Scales(ns); % I = 1:length(x)-Scales(ns)+1; for n = 1:length(I) Xn = cumsum(x(I(n):I(n)+Scales(ns))); Yn = cumsum(y(I(n):I(n)+Scales(ns))); Fxyn(n) = F_xy(Xn,Yn); end for j = 1:length(Q) q = Q(j); Fxy(j,ns) = (mean(abs(Fxyn).^(q/2))).^(1/q); end clear Fxyn end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Fxyn = F_xy(Xn,Yn) % Calculate the detrended covariance of the n-th box ResX = DetrendedResiduals(Xn); ResY = DetrendedResiduals(Yn); Fxyn =mean(ResX.*ResY); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Res = DetrendedResiduals(z) % P = POLYFIT(X,Y,N) finds the coefficients of a polynomial P(X) of % degree N that fits the data Y best in a least-squares sense. P is a % row vector of length N+1 containing the polynomial coefficients in % descending powers, P(1)*X?N + P(2)*X?(N-1) +...+ P(N)*X + P(N+1). t = reshape(1:length(z),size(z)); P = polyfit(t, z, 1); trend_z =polyval(P,t); Res = z - trend_z;